Bayesian networks for inferring the relationship between individual behavior and social influence: A case study of early 20th century British travels in China

Abstract

This study investigated the possibility of applying the Bayesian networks (BNs) in analyzing the relationship between individual behavior and social influence among early 20th-century British travelers in China. While historical studies have provided valuable details about social interactions, existing research using such studies has shown limitations in quantifying and analyzing complex relationships. This study attempts to address this gap by employing Bayesian networks (BNs) to construct a framework for modeling the probabilistic relationships between various factors influencing the travel patterns of British travelers in China in the early 20th century. These factors include political climate, economic considerations, and cultural interactions, which are sourced through historical studies, travel diaries, and other contemporary sources. The performance of the proposed Bayesian network model is evaluated using established statistical methods, including confusion matrices, cross-validation, and sensitivity analysis (SA). The results have shown the significance of the chosen model in analyzing the complex relationship selected analysis.